Added first slide deck using quarto/revealjs.

Author: jtkiley

.gitignore

@@ -12,3 +12,7 @@ data/msft_pr_coding.csv
 
 # Mac
 .DS_Store
+
+# Quarto
+*_files
+*.html

readme.markdown

@@ -61,6 +61,8 @@ There is both a notebook and a slide deck for most segments.
 The slides are available in zip archives containing Keynote and Powerpoint versions in the releases section (on Github).
 Please note that the Keynote slides are (usually) the ones actually presented.
 
+**Note:** For June 2024, I am testing a [new slide technology](https://quarto.org/docs/presentations/revealjs/) for the first slide deck only, which is included in the slides folder. A full set of slides is available as described above.
+
 
 ## Preparing for the course
 
@@ -117,7 +119,7 @@ Once your local container has been created, you can return to it using the follo
 1. On the Welcome tab, under the heading "Recent," click the "carma_python in a unique volume" link.
 
 
-## API configuration
+## API credential configuration
 
 **Note:** I will refer to the configuration instructions below during the course, but you do not need to follow these when preparing for the course.
 

slides/0a.qmd

@@ -0,0 +1,209 @@
+---
+title: "Introduction to Python for Research"
+subtitle: "0a: Introduction"
+author: "Jason T. Kiley"
+format:
+    revealjs:
+        theme: dark
+        css: _style.css
+        slide-number: true
+
+---
+
+##  {.center}
+
+::: r-fit-text
+[github.com/jtkiley](https://github.com/jtkiley/)
+:::
+
+## Related
+
+- CARMA 2020 (overlaps with this course): Introduction to Python and Content Analysis of Text. ([Github](https://github.com/jtkiley/2020_carma_python))
+- Seminar materials (overlaps with this course): Text Analysis: Planning to Publication. ([Github](https://github.com/jtkiley/text_seminar))
+- Text analysis and machine learning workshop at WU (Oct. 2018) and RSM (Oct. 2019).
+- AOM Big Data workshop with Tim Hannigan, Hovig Tchalian, and Laura Nelson. ([Github](https://github.com/jtkiley/curation_workshop))
+
+## Course Agenda
+
+- Tools: Python, packages and environments
+- Basics: Python syntax and conventions, Jupyter Notebooks
+- Data handling and project planning
+- Data gathering and assembly
+
+# Overview
+
+## Overview
+
+- What you really need to know about Python.
+- Resources for learning.
+- A brief R comparison.
+
+# What do I really need to know about Python?
+
+## Why Python?
+
+- Approachability: well-designed modern programming language that handles a lot for you.
+- Features: many things have been built already, and you simply "glue" them together.
+- Learning resources: wide popularity in academia and practice means that there are extensive resources.
+- Scalability: from your computer, to the cloud, to a computing cluster, you can use largely the same tools.
+
+## Python Fluency
+
+- [Basics]{style="color:lightblue;"}
+- [Data Preparation]{style="color:lightgreen;"}
+- [Good-enough Programming]{style="color:lightyellow;"}
+- [Software Engineering]{style="color:pink;"}
+
+## [Basics]{style="color:lightblue;"}
+
+- Skills
+    - Software: Python interpreter, Jupyter Notebooks, VS Code
+    - Variable types: strings, ints, floats
+    - Objects and methods: lists, dictionaries
+    - Packages: importing and installing
+    - Documentation: official and community
+- Time: 2-4 hours
+- Necessity: Largely unavoidable
+
+
+## [Data Preparation]{style="color:lightgreen;"}
+
+- Skills
+    - Software: pandas
+    - Reading data formats (built-in)
+    - Slicing, views, `df.loc[]`
+    - Operations on columns and rows
+    - Reshaping
+    - Merging and querying
+- Time: 1-2 days and ongoing
+- Necessity: Needed and high ROI
+
+
+## [Good-enough Programming]{style="color:lightyellow;"}
+
+- Skills
+    - Loops
+    - Writing functions
+    - Reading and writing files (the hard way)
+    - Throwing and handling exceptions
+    - Using additional packages
+    - End point: working, reusable script
+- Time: 1 week and ongoing; divisible
+- Necessity: Helpful and good ROI
+
+
+## [Software Engineering]{style="color:pink;"}
+
+- Skills
+    - Classes and inheritance\*
+    - Package development\*
+    - Version control\*
+    - Unit testing and continuous integration
+    - Cross-version support
+    - Open source contributions
+- Time: A lot
+- Necessity: Not at all; good for the field
+
+
+# What resources are available for learning?
+
+## Pandas documentation
+
+::: columns
+::: {.column width="50%" #vcenter}
+Comparison with Stata
+:::
+
+::: {.column width="50%" #vcenter}
+![](_img/0a_stata.png)
+:::
+:::
+
+::: footer
+See more: [pandas documentation](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_stata.html)
+:::
+
+## Stack Overflow
+
+::: columns
+::: {.column width="50%" #vcenter}
+Search for what you are trying to do, merging on multiple columns with different names, in this case.
+:::
+
+::: {.column width="50%" #vcenter}
+![](_img/0a_so.png)
+:::
+:::
+
+::: footer
+See more: [Stack Overflow](https://stackoverflow.com/questions/41815079/pandas-merge-join-two-data-frames-on-multiple-columns)
+:::
+
+## Python for Data Analysis
+
+::: columns
+::: {.column width="50%" #vcenter}
+Wes McKinney is the creator of pandas and other open source projects.
+:::
+
+::: {.column width="50%" #vcenter}
+![](_img/0a_book.png)
+:::
+:::
+
+::: footer
+For more: [Wes McKinney](https://wesmckinney.com/book/)
+:::
+
+
+## Other Resources
+
+- edX. Provides many courses that use or teach Python that are relevant for data work (free).
+- Self-study tracks from my seminar. Includes resources for data handling, data retrieval, machine learning.
+- YouTube. Has many content creators, covering Python, data science, and software development.
+
+# Python and R
+
+## What About R?
+
+- R is great overall, especially compared to a lot of commercial stats software.
+- Compared to Python, it is less general purpose, so some useful packages may not have analogues.
+- The syntax (from S in the 1970s) is sometimes quite arcane.
+- Best of both worlds:
+    - Gather and prep data in Python.
+    - If needed, use R for analyses.
+
+## Stack Overflow - Most Popular
+
+![](_img/0a_r.png)
+
+::: footer
+See more: [Stack Overflow](https://survey.stackoverflow.co/2023/#programming-scripting-and-markup-languages)
+:::
+
+## Stack Overflow - Most Desired
+
+![](_img/0a_r2.png)
+
+::: footer
+See more: [Stack Overflow](https://survey.stackoverflow.co/2023/#section-admired-and-desired-programming-scripting-and-markup-languages)
+:::
+
+# Getting Started
+
+## Getting Started
+
+- Two approaches (choose one):
+    - Github Codespaces (cloud)
+    - VS Code, Docker, local container
+- You'll see me use both.
+
+# Hands on
+
+## Summary
+
+- Using Python for data analysis is not exactly programming, and you already have much of the knowledge you need.
+- Capturing all of our work in code that runs is a best practice that promotes reproducibility, and that helps us most of all.
+- We will start using our container in the next segment, so make sure it is set up and ready (or ask for help).
+
+# Break

slides/_img/0a_book.png

@@ -0,0 +1,8 @@
+/*-- scss:defaults --*/
+
+
+/*-- scss:rules --*/
+
+#vcenter {
+    vertical-align: middle;
+}